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README.md

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+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:43188
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/nli-deberta-v3-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- accuracy
+- accuracy_threshold
+- f1
+- f1_threshold
+- precision
+- recall
+- average_precision
+model-index:
+- name: CrossEncoder based on cross-encoder/nli-deberta-v3-base
+  results:
+  - task:
+      type: cross-encoder-binary-classification
+      name: Cross Encoder Binary Classification
+    dataset:
+      name: paws val judge
+      type: paws-val-judge
+    metrics:
+    - type: accuracy
+      value: 0.9645748987854251
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.08707074075937271
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.9604876947392187
+      name: F1
+    - type: f1_threshold
+      value: 0.08707074075937271
+      name: F1 Threshold
+    - type: precision
+      value: 0.9470169189670525
+      name: Precision
+    - type: recall
+      value: 0.9743472285845167
+      name: Recall
+    - type: average_precision
+      value: 0.9870268561433264
+      name: Average Precision
+---
+
+# CrossEncoder based on cross-encoder/nli-deberta-v3-base
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-base) <!-- at revision 6c749ce3425cd33b46d187e45b92bbf96ee12ec7 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("cross_encoder_model_id")
+# Get scores for pairs of texts
+pairs = [
+    ['Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .', 'Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .'],
+    ['During the competition she lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .', 'During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .'],
+    ['The latter study is one of the few prospective demonstrations that environmental stress with high blood pressure and LVH remains associated .', 'The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .'],
+    ['The Marignane is located at Marseille Airport in Provence .', 'The Marignane is located in Marseille Provence Airport .'],
+    ['Birleffi was of Italian descent and Roman - Catholic in a predominantly Protestant state .', 'Birleffi was of Italian ethnicity and Roman Catholic in a predominantly Protestant state .'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .',
+    [
+        'Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .',
+        'During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .',
+        'The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .',
+        'The Marignane is located in Marseille Provence Airport .',
+        'Birleffi was of Italian ethnicity and Roman Catholic in a predominantly Protestant state .',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Binary Classification
+
+* Dataset: `paws-val-judge`
+* Evaluated with [<code>CEBinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator)
+
+| Metric                | Value     |
+|:----------------------|:----------|
+| accuracy              | 0.9646    |
+| accuracy_threshold    | 0.0871    |
+| f1                    | 0.9605    |
+| f1_threshold          | 0.0871    |
+| precision             | 0.947     |
+| recall                | 0.9743    |
+| **average_precision** | **0.987** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 43,188 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                                       | sentence_1                                                                                       | label                                                          |
+  |:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                           | string                                                                                           | float                                                          |
+  | details | <ul><li>min: 38 characters</li><li>mean: 114.71 characters</li><li>max: 200 characters</li></ul> | <ul><li>min: 42 characters</li><li>mean: 114.33 characters</li><li>max: 215 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                 | sentence_1                                                                                                                                                 | label            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .</code>                                       | <code>Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .</code>                                     | <code>0.0</code> |
+  | <code>During the competition she lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .</code>                                               | <code>During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .</code>                                            | <code>1.0</code> |
+  | <code>The latter study is one of the few prospective demonstrations that environmental stress with high blood pressure and LVH remains associated .</code> | <code>The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .</code> | <code>1.0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | paws-val-judge_average_precision |
+|:------:|:----:|:-------------:|:--------------------------------:|
+| 0.1852 | 500  | 0.3758        | -                                |
+| 0.3704 | 1000 | 0.226         | -                                |
+| 0.5556 | 1500 | 0.2176        | -                                |
+| 0.7407 | 2000 | 0.1778        | -                                |
+| 0.9259 | 2500 | 0.1757        | -                                |
+| 1.0    | 2700 | -             | 0.9826                           |
+| 1.1111 | 3000 | 0.1494        | -                                |
+| 1.2963 | 3500 | 0.1271        | -                                |
+| 1.4815 | 4000 | 0.1197        | -                                |
+| 1.6667 | 4500 | 0.1263        | -                                |
+| 1.8519 | 5000 | 0.116         | -                                |
+| 2.0    | 5400 | -             | 0.9852                           |
+| 2.0370 | 5500 | 0.1084        | -                                |
+| 2.2222 | 6000 | 0.0707        | -                                |
+| 2.4074 | 6500 | 0.0741        | -                                |
+| 2.5926 | 7000 | 0.0713        | -                                |
+| 2.7778 | 7500 | 0.0723        | -                                |
+| 2.9630 | 8000 | 0.0727        | -                                |
+| 3.0    | 8100 | -             | 0.9870                           |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.3
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+}

config.json

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+    "DebertaV2ForSequenceClassification"
+  ],
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+  "bos_token_id": 1,
+  "dtype": "float32",
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+  "hidden_act": "gelu",
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+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.2.0"
+  },
+  "share_att_key": true,
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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special_tokens_map.json

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tokenizer_config.json

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